The present invention relates generally to perioperative pain management and, more particularly to a non-invasive bedside tool for monitoring central nervous system effects of opioids, including postoperative respiratory depression.
Opioids are commonly used analgesics to manage severe pain following surgery in children. In the United States alone, approximately 6 million children undergo painful surgeries each year. However, inadequate pain relief and serious side effects from perioperative opioids occur in up to 50% of such children because clinicians continue to titrate morphine based on pain scores alone to gain satisfactory analgesia in children, despite the use of morphine for acute pain relief for over a century. Such a clinical practice fails to take into account increased central nervous system adverse effects caused with higher opioid doses. An example of a serious side effect caused by the use of opioids in children includes respiratory depression, which is a life-threatening adverse effect. These problems exist and continue to occur because of narrow therapeutic indices, large and unpredictable inter-patient variability in responses to opioids, and a lack of bedside biomarkers predictive of inter-individual pharmacodynamic variability and the morphine concentration-response relationship for acute pain management in children.
Thus, in order to optimize analgesia while avoiding unnecessary adverse effects, better and/or proactive risk prediction and tailored interventions are desired. With current standard clinical practice, it is difficult to proactively identify children at risk for serious opioid adverse effects at bedside. Safe and effective analgesia following surgery is an unmet clinical need in children as a result of our incomplete understanding of central nervous system (CNS) effects of opioids in terms of pharmacokinetics and predictive tools of opioid-induced respiratory depression.
A quantitative pupillometry (QP) system of the present disclosure may serve as a real-time tool to guide opioid titration, and has substantial potential for optimizing analgesia and reducing central nervous system adverse effects by opioids, including respiratory depression. This quantitative pupillometry system may identify children at risk for central opioid adverse effects, especially respiratory depression, before they occur clinically based on high sensitivity and reliability of serial quantitative pupillometry measures in children. Thus, the use of the quantitative pupillometry system of the present disclosure may avoid prolonged hospital stays and deaths associated with opioid-induced respiratory depression.
The illustrative quantitative pupillometry system of the present disclosure is a non-invasive innovative bedside tool for monitoring CNS effects of opioids. Opioids such as morphine cause miosis and altered pupillary reaction time. Bedside quantitative pupillometry is shown to be safe, non-invasive, well-tolerated by children of 1 to 18 years of age, and relatively easy to use with minimal training. The pupillary response to painful stimuli has been proven to be a more sensitive marker than the associated hemodynamic changes of heart rate and systolic blood pressure in children under sevoflurane anesthesia with pupillometry values showing a significant increase in pupil size immediately after the skin incision followed by a rapid decrease in pupil size immediately after the administration of alfentanil. Though quantitative pupillometry has been successfully used to measure CNS analgesic effects of opioids, it has not been studied to assess opioid's risk of CNS adverse effects such as respiratory depression in children.
Illustratively, the quantitative pupillometry system of the present disclosure includes a handheld pupillometer for use as a bedside, real-time, non-invasive tool to proactively predict an individual child's risk for postoperative respiratory depression. Preliminary perioperative morphine pharmacokinetic data, serial quantitative pupillometry, clinical outcome and genetic data from 300 children undergoing tonsillectomy suggest that inter-individual variability in response to morphine in children can be predicted and better assessed at bedside by quantitative pupillometry. Some children are at higher risk than others for respiratory depression and it is hard for clinicians to proactively identify at-risk children and tailor their use of opioids. Preoperative genotyping and monitoring of safe opioid levels are not routinely performed in children undergoing surgical procedures to identify and minimize an individual's risk with opioids. Current practice of morphine administration is based on pain scores alone and is suboptimal and risky as it does not balance against increased adverse effects including respiratory depression with higher opioid doses. Preliminary data suggest that serial quantitative pupillometry measures correlate with morphine's pharmacokinetics and can potentially help to identify children at risk of postoperative respiratory depression.
The illustrative quantitative pupillometry system is configured to tailor perioperative pain management in children to individual needs, to improve safety and reduce cost of perioperative care with the right dose of the right analgesic for each child. The illustrative system is configured to determine how serial non-invasive objective quantitative pupillometry measures correlate with morphine's pharmacokinetics and postoperative respiratory depression in children. The system is based on perioperative quantitative pupillometry measures in children correlating with morphine's pharmacokinetics and proactively predicting postoperative opioid-related respiratory depression.
The illustrative quantitative pupillometry system as a non-invasive, real-time, sensitive bedside tool to objectively measure pupil size and its reaction to light, can be an effective method of assessing CNS effects of opioids. Once morphine's pharmacokinetics and CNS pharmacodynamics are reliably and non-invasively captured with the quantitative pupillometry, it will be possible to better identify children at risk for postoperative opioid-induced respiratory depression without invasive serial pharmacokinetic sampling when lacking routine preoperative genotyping.
Operation of the quantitative pupillometry system is based on the following principles: (1) determining pupillary effects of intraoperative morphine and correlating with morphine's pharmacokinetics in children undergoing tonsillectomy; and (2) predicting postoperative opioid-induced respiratory depression in children undergoing tonsillectomy with perioperative quantitative pupillary measures.
The quantitative pupillometry system of the present disclosure is an innovative bedside biomarker of CNS effects and pharmacokinetics of opioids that narrows the current critical knowledge gap by proactively identifying children at risk for postoperative respiratory depression. This enables proactive risk prediction and thereby personalized use of the right analgesics at the right dose in children to maximize pain relief while minimizing adverse effects.
According to an illustrative embodiment of the present disclosure, a quantitative pupillometry system for predicting postoperative respiratory depression includes a pupillometer having an image acquisition device and a stimulus light source, the image acquisition device being configured to detect pupillary effects from a pupil of a patient in response to light from the stimulus light source being applied to the pupil. The quantitative pupillometry system further includes a memory unit storing opioid pharmacokinetic data, and a processor in communication with the pupillometer and the memory unit. The processor includes a pharmacokinetic association module for associating the opioid pharmacokinetic data with anticipated pupillary effects, and a respiratory depression prediction module for predicting a probability of opioid-related respiratory depression by comparing the anticipated pupillary effects from the pharmacokinetic association module and the detected pupillary effects from the pupillometer. The quantitative pupillometry system further includes a user interface in communication with the processor, the user interface being configured to provide the prediction of the probability of opioid-related respiratory depression from the respiratory depression prediction module to a user.
According to a further illustrative embodiment of the present disclosure, a quantitative pupillometry system for predicting a probability of at least one opioid-related central effect includes a pupillometer including an image acquisition device and a stimulus light source, the image acquisition device being configured to detect pupillary effects from a pupil of a patient in response to light from the stimulus light source being applied to the pupil. The quantitative pupillometry system further includes a processor in communication with the pupillometer, the processor including an opioid-related central effect prediction module for predicting a probability of at least one opioid-related central effect in response to the pupillary effects detected by the pupillometer. The quantitative pupillometry system further includes a user interface in communication with the processor, the user interface being configured to display the prediction of the probability of the at least one opioid-related central effect from the processor to a user.
According to another illustrative embodiment of the present disclosure, a method of predicting a probability of at least one opioid-related central effect includes the steps of administering an opioid to a patient, stimulating a pupil of the patient, and acquiring an image of the stimulated pupil. The method further includes the steps of detecting pupillary effects of the pupil, predicting a probability of at least one opioid-related central effect based upon the detected pupillary effects, and providing the predicted probability of the at least one opioid-related central effect to a user.
In various aspects of the above embodiments, the memory unit is configured to receive and process the opioid pharmacokinetic data. Furthermore, in various aspects, the at least one opioid-related central effect includes respiratory depression, sedation and/or vomiting.
Additional features and advantages of the present invention will become apparent to those skilled in the art upon consideration of the following detailed description of the illustrative embodiments exemplifying the best mode of carrying out the invention as presently perceived.